Deep Question-Answering (DeepQA) is an application of and combination of the fields of advanced Natural Language Processing, Information Retrieval, Knowledge Representation and Reasoning, and Machine Learning technologies to the field of open-domain question answering, all executing on a suitable computing platform. Such methods of hypothesis generation, evidence gathering, analysis, and scoring may be effectively executed by a wide range of computing platforms.
International Business Machines Corporation (IBM) has published details of one such computing system, including computing methods and technologies that are able to assist humans with certain types of semantic query and search operations, such as the type of natural question-and-answer paradigm of a medical environment. IBM researchers and scientists have been working on DeepQA methods that are able to understand complex questions posed (and input) in natural language, and are able to answer the question with enough precision, confidence, and speed to augment human handling of the same questions within a given environment, such as a medical inquiry and diagnostic paradigm, strategic planning, etc.
Similarly, IBM has also published computing methods which combine semantic elements with information search elements to form Unstructured Information Management Architecture (UIMA), which is now maintained as an open source project by the Apache organization.
Whereas ample information is available in the public domain regarding DeepQA and UIMA, the present disclosure presumes those ordinarily skilled in the art may access and apply that information to realized embodiments of the following invention.
These types of search and query systems, including the prior art systems which perform searches in a non-DeepQA manner, may produce an overwhelming amount of answer data due to the ever-increasing volume of information which can be searched. The search and answer prioritization methods of these systems generally operates on the relevance of the results to the query.
The present inventors have realized, and expressly do not disclaim as a prior known problem in the art, that Natural Language Processing (NLP) question/answer systems analyze Natural Language (NL) input against a corpora of data to identify the ‘correct’ answer to the question. This approach is sufficient for backward-looking questions in which answers are more acute and factual in nature. But, as the inventors looked to employ these DeepQA systems for more forward-looking decision-making, the inventors discovered that the correct or best answer may be more obtuse and personal. This problem does not yet seem to be recognized in the art, to the best of the inventors' knowledge.
The present inventors have realized that the current approaches are insufficient in this emerging domain. Humans are unique individuals varying widely in their view of the world (religious, political, societal, cultural, etc.). Therefore, there exists a need in the art for humans interfacing with these systems to influence the generated answers beyond the factual data (e.g. relevance) fed to the system and contained in the corpora.
For example, an oncology patient's medical information and history is fed to a query system, such as a DeepQA system, for analysis against the most recent guidelines, research and treatment regimen to generate the “best available” treatment options. The query system, based solely on this patient's information, might always generate a treatment plan consisting of chemotherapy as the correct or best answer due to superior effectiveness data. But, what if the patient is adamantly opposed to undergoing chemotherapy for a variety of reasons? The oncology professional must now manually cull through the answers to look for answers which avoid or minimize the use of chemotherapy, thereby rendering the traditional query system of limited use. This is where existing solutions fall short, and where the need exists for an invention which would allow for the patient to communicate to the system in NL that “I want to avoid chemotherapy”. Such an invention, according to the present inventors' discoveries, would then attempt to identify the best sequence of treatment options in accordance with the user's preferences.
In a second example from a different field of use, imagine that an investment portfolio manager who is looking to invest in overseas commodity supplier trading might ask a traditional query system about which commodities are poised for growth and which countries stand to benefit. Now imaging that her clients are ecologically conscious and they prefer investing in institutions employing “green technology as much as possible”. Traditional query systems may not be able to employ such a fuzzy requirement in their answer ranking and relevance engines, so there is a need in the art identified by the present inventors and not recognized by those skilled in the art for a mechanism to allow such a portfolio manager to indicates to the query system a user preference in natural language such as “prefer eco-friendly institutions and countries the most”.